By Josephb F. Hair, G. Tomas M. Hult, Christian M. Ringle, Marko Sarstedt

ISBN-10: 1452217440

ISBN-13: 9781452217444

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Additional info for A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)

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252-276). Cheltenham, UK: Edward Elgar. Joreskog, K. , & Wold, H. (1982). The ML and PLS techniques for model­ ing with latent variables: Historical and comparative aspects. In H. Wold & K. G. ), Systems under indirect observation, Part I (pp. 263-270). Amsterdam: North-Holland. Lohmoller, J. B. (1989). Latent variable path modeling with partial least squares. Heidelberg: Physica. Mateos-Aparicio, G. (2011). Partial Least Squares (PLS) methods: Origins, evolution, and application to social sciences.

The residual vari­ ance) of the endogenous constructs. , path model relationships) that maximize the R2 values of the (target) endogenous constructs. This feature achieves the prediction objective of PLS-SEM. PLS-SEM is therefore the pre­ ferred method when the research objective is theory development and explanation of variance {prediction of the constructs). For this reason, PLS-SEM is regarded as a variance-based approach to SEM. Chapter 1 An Introduction to Structural Equation Modeling 15 Note that PLS-SEM is similar but not equivalent to PLS regres­ sion, another popular multivariate data analysis technique.

It should be noted, however, that "soft" is attributed only to the distributional assumptions and not to the concepts, models, or estimation tech­ niques (Lohmoller, 1989). , 2009). , 2010). The PLS-SEM algorithm generally requires metric data for the mea­ surement model indicators. , quasi-metric scales; Mooi & Sarstedt, 2011) and with binary coded data. The use of binary coded data is often a means of including categorical control variables or mod­ erators in PLS-SEM models. , 2012b). , 2011).

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A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) by Josephb F. Hair, G. Tomas M. Hult, Christian M. Ringle, Marko Sarstedt


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